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country. The large dots with red circles around them represent hubs. A
hub
represents a person with high connectivity and a high “betweenness” score. The
cluster in
Figure 2.11
contains geographic variety, which is critical to prove the
hypothesis about geographic boundary spanners. One person in this graph has
an unusually high score when compared to the rest of the nodes in the graph.
The data scientist identified this person and ran a query against his name within
the analytic sandbox. These actions yielded the following information about this
research scientist (from the social graph), which illustrated how influential he was
within his business unit and across many other areas of the company worldwide:
• In 2011, he attended the ACM SIGMOD conference, which is a top-tier
conference on large-scale data management problems and databases.
• He visited employees in France who are part of the business unit for
EMC's content management teams within Documentum (now part of the
Information Intelligence Group, or IIG).
• He presented his thoughts on the SIGMOD conference at a virtual
brownbag session attended by three employees in Russia, one employee in
Cairo, one employee in Ireland, one employee in India, three employees in
the United States, and one employee in Israel.
• In 2012, he attended the SDM 2012 conference in California.
• On the same trip he visited innovators and researchers at EMC federated
companies, Pivotal and VMware.
• Later on that trip he stood before an internal council of technology leaders
and introduced two of his researchers to dozens of corporate innovators
and researchers.
This finding suggests that at least part of the initial hypothesis is correct; the data
can identify innovators who span different geographies and business units. The
team used Tableau software for data visualization and exploration and used the
Pivotal Greenplum database as the main data repository and analytics engine.
2.8.5 Phase 5: Communicate Results
In Phase 5, the team found several ways to cull results of the analysis and identify
the most impactful and relevant findings. This project was considered successful in
identifying boundary spanners and hidden innovators. As a result, the CTO office
launched longitudinal studies to begin data collection efforts and track innovation
results over longer periods of time. The GINA project promoted knowledge sharing
